def main(_):
# Set configuration
config = AttrDict(default_config())
# Build one hot mnist model.
model = benchmark_model.build_tf_one_hot_model(batch_size=config.batch_size,
use_bias=config.use_bias,
activation=config.activation)
# Load one hot mnist data.
(x_train, y_train), (x_test, y_test) = benchmark_model.load_one_hot_data(dataset=config.dataset)
# Testing whether the dataset have correct shape.
assert x_train.shape == (60000, 784)
assert y_train.shape == (60000, 10)
# Minimize model's loss with NMF optimizer.
# optimizer = NMFOptimizer(config)
optimizer = NMFOptimizer(config=config)
train_op = optimizer.minimize(model.frob_norm)
# Minimize model's loss with Adam optimizer.
bp_optimizer = tf.train.AdamOptimizer(config.learning_rate)
bp_train_op = bp_optimizer.minimize(model.cross_entropy)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(1000):
x, y = benchmark_model.batch(x_train, y_train, batch_size=config.batch_size)
loss, _ = sess.run([optimizer.autoencoder_loss, optimizer.autoencoder_train_op],
feed_dict={model.inputs: x})
print('\rloss {}'.format(loss), end='', flush=True)
print()
_train_and_test = functools.partial(train_and_test,
sess=sess, model=model,
x_train=x_train, y_train=y_train,
x_test=x_test, y_test=y_test,
batch_size=config.batch_size)
print('NMF-optimizer')
# Train with NMF optimizer.
_train_and_test(train_op, num_iters=config.num_mf_iters)
print('Adam-optimizer')
# Train with Adam optimizer.
_train_and_test(bp_train_op, num_iters=config.num_bp_iters)
def main(_):
# Set configuration
config = AttrDict(default_config())
# Build one hot mnist model.
model = benchmark_model.build_tf_cross_entropy_model(
batch_size=config.batch_size,
use_bias=config.use_bias,
activation=config.activation)
# Load one hot mnist data.
(x_train, y_train), (x_test, y_test) = benchmark_model.load_one_hot_data(
dataset=config.dataset)
# Testing whether the dataset have correct shape.
assert x_train.shape == (60000, 784)
assert y_train.shape == (60000, 10)
# Minimize model's loss with NMF optimizer.
# optimizer = NMFOptimizer(config)
optimizer = NMFOptimizer()
train_op = optimizer.minimize(model.cross_entropy)
# Minimize model's loss with Adam optimizer.
bp_optimizer = tf.train.AdamOptimizer(config.learning_rate)
bp_train_op = bp_optimizer.minimize(model.cross_entropy)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
_train_and_test = functools.partial(train_and_test,
sess=sess,
model=model,
x_train=x_train,
y_train=y_train,
x_test=x_test,
y_test=y_test,
batch_size=config.batch_size)
print('NMF-optimizer')
# Train with NMF optimizer.
_train_and_test(train_op, num_iters=config.num_mf_iters)
print('Adam-optimizer')
# Train with Adam optimizer.
_train_and_test(bp_train_op, num_iters=config.num_bp_iters)